Experiments in climatological time series analysis using deep learning.

Loss (MSE) 0.00037 on SSN with 64-layer LSTM and 400 epochs. See the notebook.

Interestingly, the network is trained on 66% of the SSN data but correctly predicts the weakness of solar cycle 24.

Next step: predict solar cycles 25, 26, 27!

1. create a generic LSTM framework/notebook
2. analyze SSN (Solar Sunspot Numbers) monthly series
3. analyze Global/Local Datasets (temperature, precipitation, etc)
4. analyze climatic indices (ENSO, etc)

1. modify the network in order to accept Continuous Wavelet Transform output
2. generate signal from predicted CWT spectra

John Abbot et al.: The application of machine learning for evaluating anthropogenic versus natural climate change, GeoResJ (2017). DOI: 10.1016/j.grj.2017.08.001

Qin Zhang et al.: Prediction of Sea Surface Temperature using Long Short-Term Memory. arXiv:1705.06861 [cs.CV]

Bao W, Yue J, Rao Y: A deep learning framework for financial time series using stacked autoencoders and long-short term memory. Podobnik B, ed. PLoS ONE. 2017;12(7):e0180944. doi:10.1371/journal.pone.0180944

Franco Zavatti: Clima, Reti Neurali, Dati di Prossimità e Analisi Spettrali. http://www.climatemonitor.it/?p=46061

https://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/

http://www.willfleury.com/machine-learning/forecasting/lstm/2017/09/01/short-term-forceasting-lstm.html

https://github.com/simaaron/kaggle-Rain

https://thesai.org/Downloads/Volume8No2/Paper_43-Prediction_by_a_Hybrid_of_Wavelet_Transform.pdf

SSN Sunspot Number - Source: WDC-SILSO, Royal Observatory of Belgium, Brussels